Systems Methods Circuits and Associated Computer Executable Code for Digital Catalog Augmentation

ABSTRACT

Disclosed are methods, circuits, devices, systems and functionally associated computer executable code for digital catalog augmentation. A digital catalog interface module reads from a digital catalog data storage, directly or indirectly, one or more catalog data records constituting an offer listing within a digital catalog, wherein the offer listing may include a description of a specific product or service offering and/or links to execute a transaction relating to the offering. The system includes a Review Criteria and Sentiment Extractor (RCSE) to identify and convert one or more reviews posted on a review forum into one or more data records used to augment the offer listing within the digital catalog.

RELATED APPLICATIONS

The present application claims priority from U.S. Provisional PatentApplication No. 62/275,252 filed on Jan. 6, 2016 and titled: ‘SystemsMethods Circuits and Associated Computer Executable Code for OpinionMining on Consumer Reviews and Applications thereof’. The fulldisclosure of the 62/275,252 Provisional Patent Application is herebyincorporated by reference in its entirety.

FIELD OF THE INVENTION

The present invention generally relates to the fields of online contentpublishing. More specifically, the present invention relates to methods,circuits, devices, systems and functionally associated computerexecutable code for augmenting digital catalogs, for example, augmentingonline catalogs using data mining and automated analysis of offeringreviews.

BACKGROUND

Opinions on products left on e-commerce websites and social media siteshave hidden golden dust for merchants. Often consumer reviews areavailable in large quantity. Whilst it is difficult for a merchant toread all of the reviews available, limiting the number to a few canresult into a biased consumer view.

Sentiment analysis is useful to understand overall mood in the marketfor specific things. Typically the sentiment summarizers, or featurebased recommenders, work by maintaining a set of features, preidentified from the product descriptions. In such systems, vectors areused for maintaining counts of likes and dislikes, often withtime-series data. Such systems fail to accommodate any new featuresother than those pre identified. This is where opinion mining ishelpful.

Some of the existing solutions use keywords to search and analyze theresults, wherein consumers may provide their aspired preferences (e.g.specifying symptoms, compatibility criteria and subjective hints;requesting trendy products and those that are better than user specifiedbrands). It is still beyond most merchant's capability to: collect suchinformation in today's fast pace and large volume e-commerceenvironments, analyze the information, update product catalogs based onthe analysis and provide a sophisticated search system that understandsconsumer's queries.

Accordingly, there remains a need, in the field of online contentpublishing and e-commerce, for solutions that may process unstructuredtext, such as customer reviews/comments, to identify: new featuresbeyond those pre identified from the product descriptions, the sentimentof customers (e.g. appraisal expressions) towards the newly identifiedfeatures of the product, the general likes and dislikes of consumers andconsumer groups, strengths and weaknesses of products and services beingsold, consumers’ wish lists, other competitors' strengths and weaknessesand the like. Such insights can help merchants identify the right ‘callsfor actions’, for example, by allowing merchants to automatically enrichthe descriptions of their offerings within digital catalogs and/or toreport, remove, or present alternatives, to offerings triggeringnegative reviews and sentiment.

SUMMARY OF THE INVENTION

The present invention includes methods, circuits, devices, systems andfunctionally associated computer executable code for augmenting adigital catalog, which catalog may comprise network accessible datarepresenting one or more product or service offerings (herein afterreferred to as offering or offerings). A digital catalog according toembodiments may include and/or otherwise be associated with ane-commerce transaction system which allows a catalog viewer to purchasean offering listed in the catalog. A digital catalog according toembodiments may also include, or be otherwise functionally associatedwith, one or more online review zones where purchasers, consumers orusers of a listed offering may submit for posting a review providing an(preferably personal) assessment of one or more characteristics orattributes of the offering. According to some embodiments of the presentinvention, there may be provided a digital catalog augmentation systemwhich may modify digital catalog data relating to a specific digitalcatalog offering as a function of an automated analysis of one or morereviews of the specific offering.

A review scrapper integral or otherwise functionally associated with thedigital catalog augmentation system may access and/or otherwise readreviews of one or more offerings listed in a digital catalog. Thescrapper may read reviews posted on the digital catalog review zone, ona review zone linked to or otherwise associated with the digitalcatalog, and/or to any Internet accessible server where reviews ofproducts or services offered on the digital catalog may be posted.

According to some embodiments, review zones, Internet server postedreviews and/or other sources of reviews linked to, or otherwiseassociated with, the digital catalog may further include: (1) any thirdparty services allowing merchants, retailers, producers, ordistributers, to collect reviews, comments, opinions and/or feedback;(2) social media sites; and/or (3) chat scripts exchanged between usersand/or between users and agents.

An automated review analyzer integral or otherwise functionallyassociated with the digital catalog augmentation system may include: (1)a Review Text Normalizer for normalizing and correcting text of consumerreviews; and/or (2) a Review Criteria and Sentiment Extractor foridentifying, extracting and characterizing: (a) the source, targetand/or features of consumer reviews/comments, and/or (b) new criteria,relating to offering(s) in the digital catalog, and the sentimentexpressed towards the new criteria within the review/comment.

According to some embodiments, the System's Review Text Normalizer may:(1) correct spelling mistakes within the text of consumer reviews; (2)correct grammar mistakes within the text of consumer reviews; and/or (3)handle mixed code language within consumer reviews, in which multiplelanguages are used within the same text section (e.g. same sentence,same paragraph), by correcting transliterated language at leastpartially based on domain specific spell correction.

According to some embodiments, the System's Review Criteria andSentiment Extractor may identify, within the normalized and correctedtext, new criteria by which to characterize an offering within a digitalcatalog, wherein new criteria may include: (1) features not mentioned inthe catalog; (2) suitability and/or use cases not mentioned in catalog;(3) compatibility and/or use combinations, with other products, notmentioned in the catalog; and/or (4) possible outcomes of usage, etc.The system's Review Criteria and Sentiment Extractor may identify and/orextract the sentiment expressed, within the text of the customerreview/comment, towards the identified new criteria.

According to some embodiments, the system's one or more Feedback Modulesand/or Applications may infer and utilize knowledge from reviews andproduct descriptions for any combination of the following actions:

(1) Removing, suspending and/or deprioritizing the showing orpresentation of an offering within the digital catalog; wherein removed,suspended and/or deprioritized catalog offerings may include offeringsthat received a certain number of harsh/negative reviews and/orofferings that received reviews including a negative sentiment beyond apredefined threshold of negativity level.

(2) Augmenting the digital catalog with data records including newcriteria and sentiment relating to offerings therein, as expressedwithin one or more positive reviews. According to some embodiments,augmenting a digital catalog may refer to any combination of thefollowing actions: (a) modifying digital catalog records to be morecorrect, attractive and/or to include up to date specific informationabout the product/service of the digital catalog offering(s); (b)replacing digital catalog records with more current and/or up to daterecords, wherein more current and/or up to date information may relateto product/service specific information/specifications; and/or (c)appending additional information to existing digital catalog recordsand/or appending new records.

(3) Identifying, within a review with a negative sentiment, the specifictargeted feature(s) of the offering towards which the negative sentimentwas expressed and augmenting the digital catalog, by auto-responding tothe review with a negative sentiment with a listing/presentation ofother versions of the offering (e.g. alternative versions of products),wherein the listed/presented other versions of the offering may includevariations, or different options, to the specific feature(s) of theoffering that were the target of the negative sentiment.

And/or (4) generating reports, or descriptions/updates, relating toreviews including negative sentiment(s); wherein generated reports maybe relayed to a retailer and/or a producer of the digital catalogoffering's product or service which is the subject of the review, thusproviding the retailer/producer with details of the negative sentimentand/or the criteria and possibly product/service features to which itrelates, allowing him to correct or improve specific aspects of theproduct or service of the offering. Negative sentiment reviewreports/descriptions/updates may, for example, include: (a) details ofthe corresponding catalog offering; (b) details of the product/servicefeatures which were the target of the review; and/or (c) new criteriaaddressed and identified in the review and sentiment thereof.

According to some embodiments, the system's one or more Feedback Modulesand/or Applications, as part of, or in parallel to, augmenting thedigital catalog, may perform enrichment on the extracted review/commentdata to be augmented into the digital catalog offering(s) records.Terms, titles and/or descriptions extracted from a review/comment may besubstituted with corresponding terms, expressions, synonyms, parallelterms and/or semantic normalizations. For example: the word ‘durable’may be substituted with the expression ‘long lasting’; the term ‘child’may be substituted with word ‘kid’; the title ‘a 13 years old boy’ maybe substituted with ‘teenager’; and or the term ‘wife’ may besubstituted, or broadened, to ‘female’(gender), ‘woman’ and ‘adult’.Substitute terms or descriptions may be registered to a system datastore in addition and association to their respective terms, whereinsome or all of the registered substitute terms may be augmented intocorresponding digital catalog offering(s) records. The registered and/oraugmented substitute terms may be selected at least partially based onan estimation of their positive effect on a potential customer to thecorresponding offering(s).

According to some embodiments, the system may assess the credibilityand/or trustworthiness of specific digital catalog offerings reviewersand may allocate weights to specific reviews' criteria and sentimentsbased thereof. Credibility and/or trustworthiness assessment of areviewer may be based on any combination of the following factors: (1)Whether the reviewer is estimated to be an end-user of the reviewedoffering's product or service or whether the review is based on a 3^(rd)party/person testimony, wherein end-user reviews may be allocated ahigher weight; (2) Whether the reviewer is estimated to be an expert inthe field of the offering's product or service, or whether he is aprivate/unprofessional user, wherein expert reviews may be allocated ahigher weight; (3) Whether the reviewer, based on his history ofreviews, tends to focused on a specific domain(s) of digital catalogofferings (e.g. mobile communication devices), or whether his history ofreviews relates to a substantially wide range of offerings'fields/domains, wherein reviews made by reviewers having a moredomain/field focused reviews history may be allocated a higher weight;and/or (4) Whether the reviewer, based on his history of reviews orbased on a specific review made, tends to provide, or provided aspecific, concise review(s), wherein more concise reviews and/or reviewsof more concise reviewers may be allocated a higher weight.

According to some embodiments, customer reviews/comments, whetherpositive or negative, may be filtered out from consideration. Based onthe analysis of the content of the review/comment and/or based on theassessed credibility or trustworthiness of the reviewer which is thesource of the review, as described above. Filtered out reviews/commentsmay, for example, include: (1) reviews/comments estimated to be fake;(2) reviews/comments estimated to have been made for fun or as a joke;and/or (3) reviews/comments estimated to have been made for politicalreasons.

BRIEF DESCRIPTION OF THE DRAWINGS

The subject matter regarded as the invention is particularly pointed outand distinctly claimed in the concluding portion of the specification.The invention, however, both as to organization and method of operation,together with objects, features, and advantages thereof, may best beunderstood by reference to the following detailed description when readwith the accompanying drawings:

FIG. 1A, is a block diagram of an exemplary system for Automated DigitalCatalog Augmentation in accordance with some embodiments of the presentinvention;

FIG. 1B is a block diagram of the exemplary system for Automated DigitalCatalog Augmentation of FIG. 1A, further comprising an offering(s)related sentiment reporting-logic unit/module for compiling and relayingoffering related sentiment reports, in accordance with some embodimentsof the present invention;

FIG. 1C is a flowchart of the steps executed by the system of FIGS. 1Aand 1B as part of an exemplary process for Automated Digital CatalogAugmentation, in accordance with some embodiments of the presentinvention;

FIG. 2A shows, in greater detail, an exemplary: review/comment scrapper,review text normalizer and review criteria and sentiment extractor, inaccordance with some embodiments of the present invention;

FIG. 2B is a flowchart, showing the steps executed as part of anexemplary process for review text normalization, in accordance with someembodiments of the present invention;

FIG. 2C is a flowchart, showing the steps executed as part of anexemplary process for review criteria and sentiment extraction, inaccordance with some embodiments of the present invention;

FIG. 2D is a flowchart, showing the steps executed as part of anexemplary process for review source trustworthiness estimation, inaccordance with some embodiments of the present invention;

FIG. 3A shows, in greater detail, an exemplary ‘catalog augmentationand/or ‘offering related sentiment reporting logic’ unit/module, inaccordance with some embodiments of the present invention;

FIG. 3B is a flowchart, showing the steps executed as part of anexemplary process for catalog augmentation and sentiment reporting, inaccordance with some embodiments of the present invention;

FIG. 4A shows a digital catalog product description table, for anexemplary multipurpose pair of bicycle, prior to a review basedaugmentation/enrichment process, in accordance with some embodiments ofthe present invention;

FIG. 4B shows a system scrapped review of the multipurpose pair ofbicycle, in accordance with some embodiments of the present invention;

FIG. 4C shows exemplary criteria and sentiments, extracted from themultipurpose pair of bicycle review, in accordance with some embodimentsof the present invention; and

FIG. 4D shows a digital catalog product description table, for anexemplary multipurpose pair of bicycle, following to a review basedaugmentation/enrichment process, in accordance with some embodiments ofthe present invention.

It will be appreciated that for simplicity and clarity of illustration,elements shown in the figures have not necessarily been drawn to scale.For example, the dimensions of some of the elements may be exaggeratedrelative to other elements for clarity.

DETAILED DESCRIPTION

In the following detailed description, numerous specific details are setforth in order to provide a thorough understanding of some embodiments.However, it will be understood by persons of ordinary skill in the artthat some embodiments may be practiced without these specific details.In other instances, well-known methods, procedures, components, unitsand/or circuits have not been described in detail so as not to obscurethe discussion.

Unless specifically stated otherwise, as apparent from the followingdiscussions, it is appreciated that throughout the specificationdiscussions utilizing terms such as “processing”, “computing”,“calculating”, “determining”, or the like, may refer to the actionand/or processes of a computer, computing system, computerized mobiledevice, or similar electronic computing device, that manipulate and/ortransform data represented as physical, such as electronic, quantitieswithin the computing system's registers and/or memories into other datasimilarly represented as physical quantities within the computingsystem's memories, registers or other such information storage,transmission or display devices.

In addition, throughout the specification discussions utilizing termssuch as “storing”, “hosting”, “caching”, “saving”, or the like, mayrefer to the action and/or processes of ‘writing’ and ‘keeping’ digitalinformation on a computer or computing system, or similar electroniccomputing device, and may be interchangeably used. The term “plurality”may be used throughout the specification to describe two or morecomponents, devices, elements, parameters and the like.

Some embodiments of the invention, for example, may take the form of anentirely hardware embodiment, an entirely software embodiment, or anembodiment including both hardware and software elements. Someembodiments may be implemented in software, which includes but is notlimited to firmware, resident software, microcode, or the like.

Furthermore, some embodiments of the invention may take the form of acomputer program product accessible from a computer-usable orcomputer-readable medium providing program code for use by or inconnection with a computer or any instruction execution system. Forexample, a computer-usable or computer-readable medium may be or mayinclude any apparatus that can contain, store, communicate, propagate,or transport the program for use by or in connection with theinstruction execution system, apparatus, or device, for example acomputerized device running a web-browser.

In some embodiments, the medium may be an electronic, magnetic, optical,electromagnetic, infrared, or semiconductor system (or apparatus ordevice) or a propagation medium. Some demonstrative examples of acomputer-readable medium may include a semiconductor or solid statememory, magnetic tape, a removable computer diskette, a random accessmemory (RAM), a read-only memory (ROM), a rigid magnetic disk, and anoptical disk. Some demonstrative examples of optical disks includecompact disk-read only memory (CD-ROM), compact disk-read/write(CD-R/W), and DVD.

In some embodiments, a data processing system suitable for storingand/or executing program code may include at least one processor coupleddirectly or indirectly to memory elements, for example, through a systembus. The memory elements may include, for example, local memory employedduring actual execution of the program code, bulk storage, and cachememories which may provide temporary storage of at least some programcode in order to reduce the number of times code must be retrieved frombulk storage during execution. The memory elements may, for example, atleast partially include memory/registration elements on the user deviceitself.

In some embodiments, input/output or I/O devices (including but notlimited to keyboards, displays, pointing devices, etc.) may be coupledto the system either directly or through intervening I/O controllers. Insome embodiments, network adapters may be coupled to the system toenable the data processing system to become coupled to other dataprocessing systems or remote printers or storage devices, for example,through intervening private or public networks. In some embodiments,modems, cable modems and Ethernet cards are demonstrative examples oftypes of network adapters. Other suitable components may be used.

Functions, operations, components and/or features described herein withreference to one or more embodiments, may be combined with, or may beutilized in combination with, one or more other functions, operations,components and/or features described herein with reference to one ormore other embodiments, or vice versa.

The present invention generally relates to the augmentation of a digitalcatalog based on customer or consumer reviews. Throughout thespecification, discussions utilizing terms such as “review(s)”,“comment(s)”, “feedback”, “opinion”, or the like, may refer to any formof input(s), received or retrieved, relating to any characteristics orproperties of a digital catalog offering(s). The source of such reviewsmay be: a customer, a purchaser, a consumer, a user, a reviewer, anautomatic system or robot and/or any other digital catalog offering(s)associated subject or machine—or group thereof.

The present invention includes systems, methods, circuits, andassociated computer executable code for opinion mining on consumerreviews and applications thereof. According to some embodiments of thepresent invention, a system for opinion mining on consumer reviews andapplications thereof may comprise: (1) a Review Text Normalizer fornormalizing and correcting text of consumer reviews; (2) a ConsumerReview Processor for extracting and characterizing the source, targetand/or features of consumer reviews and opinions; and/or (3) one or moreFeedback Modules and/or Applications for inferring and utilizingknowledge from reviews and product descriptions.

According to some embodiments, a Review Text Normalizer may comprise:

(a) a Consumer Review Spelling Corrector for ensuring product domainspecificity and higher accuracy in recognizing misspelled features,wherein spellchecking is based on limited indexing of domain specificdictionaries and product catalogs.(b) a Consumer Review Grammar Corrector for handling grammaticalinconsistencies in consumer reviews by utilizing shallow parsing at thephrase level, for example: “blue color I like not”, “I do not like itsblue color”, wherein as long as certain words of interest appear withina close proximity words may be shuffled around their respective headwords, to convey meaning and opinion.And/or (c) a Transliterated Language Corrector for dealing with mixedcode language in consumer reviews, by referencing knowledge base invarious languages, converting transliterated reviews, or sectionsthereof, into original scripts, utilizing domain specific spellcorrection to obtain the nearest possible words and relaxed grammaticalrules to figure out the associated opinion of consumers.

According to some embodiments of the present invention, a ConsumerReview Processor may comprise:

(a) A Reviewer Analysis Classification and Characterization Module forprocessing consumer reviews to extract source and estimate if a revieweris trustworthy/useful source, his review's weight, and/or whether ho isan expert and/or a genuine consumer.According to some embodiments a Reviewer Analysis Classification andCharacterization Module for processing consumer reviews may implementone or more of the following methods and techniques, and/or anycombination thereof, for reviewer/review analysis and characterization:

-   -   According to some embodiments, considering a given reviewer a        trustworthy source may at least partially be established based        on the level of similarity of the given reviewer's review, and        the recommendation(s) it includes, to what a majority of other        reviewers say and recommend for the majority of the features of        the reviewed product. Whether, and/or to what level, the review        is in line with what others say may be established by        calculating sentiment score (described hereinafter and in        Appendix A) for every feature mentioned in the given reviewer's        review and comparing it against the overall ‘other reviews        averages’ of the individual features.    -   According to some embodiments, considering a given reviewer a        trustworthy source may at least partially be established based        on whether his reviews are endorsed by other        reviewers/consumers, or based on the number of endorsements his        reviews receive.    -   According to some embodiments, considering a given frequent        reviewer a trustworthy source may at least partially be        established based on analysis of his posts across different        reviews, assessment of whether his posts lead to specific        domains, and, if his posts do lead to specific domains, the        reviewer may be considered a trustworthy source for at least        some of those specific domain(s). Reviews on/in other        topics/domains by the same reviewer may be treated as any other        ‘regular’ consumer review.    -   According to some embodiments, considering a given reviewer a        trustworthy source may at least partially be established based        on analysis of the reviewer's ‘author name’ and possibly of        additional information about the reviewer (e.g. organization        name, designation, location) available in the review metadata,        and comparing the analyzed data with information available in        public Linked Open Datasets (LODs) such as, but not limited to,        DBPedia, Yago, Freebase etc. If the review was collected from a        website/web-location other than the e-commerce website selling        the product, and/or if the identity of the reviewer can be        established (e.g. it is listed in one of the (LODs)), than such        a person/entity may be considered a trustworthy source. In the        absence of a successful reviewer identification, the review may        receive the same weights as any other ‘regular’ review on the        website.    -   According to some embodiments, considering a given reviewer a        trustworthy source, and/or the usefulness of a given review, may        at least partially be established based on whether the review        was posted by an end-user of the product, wherein reviews made        by a reviewer estimated to be an end-user are considered more        useful and/or trustworthy. Various factors may be taken into        consideration for establishing whether a given review was posted        by an end-user or not, for example: (i) the presence of first        person pronouns (e.g. I, We, My, Our) and verbs indicating        experience of using a product (e.g. I tried, We found, In our        experience, We used); (ii) the use of non-technical vocabulary        in the review, as real consumers tend to focus on the use case        of specific features (e.g. commenting on clarity of picture        rather than talking about aperture when commenting on a camera);        and/or (iii) the level of the reviewer's distancing of himself        from the use of difficult/technical vocabulary, wherein a level        of similarity (e.g. a cosine similarity) between the text of the        review and the product description is calculated; assuming that        the more technical the review is the less likely it is that it        was written by an end-user, and based on the calculated        similarity level, it may be established whether the review was        posted by a real consumer or a professional.    -   According to some embodiments, considering a given reviewer a        trustworthy source, and/or the usefulness of a given review, may        at least partially be established based on the length of the        review. As reviews tend to be short if genuine, longer reviews        with presence of second or third person pronouns (e.g. you,        they) may suggest that the reviews are posted by professional        writers.        (b) A Pronominal Co-reference Resolver for identifying whether        an opinion is about the product overall or specific features by        identifying pronouns and the main subjects which the pronouns        are associated with. If no association can be established        between the found pronouns and targets, the product itself may        be considered as a target. Phrases without any target in the        same sentence or in the nearby context may be automatically        associated with the main product itself.        (c) A Product Parts and Features Identifier for identifying        parts or features of products being discussed in consumer        reviews. Consumer comment on a specific attribute without        mentioning the actual value, the value may be obtained from the        product's specifications. For example, “its color is . . . ”, “I        don't like its material”, “size could have been bit . . . ” etc.        Here, values of the mentioned attributes may be obtained from        the product's specifications, for example by cross correlating,        or identifying, the color/material/size attribute in the        consumer review/comment to the item (e.g. product part) he is        viewing or examining.        (d) An Idiom Replacer for handling idioms in consumer reviews,        wherein based on a dictionary of idioms in different languages,        along with their corresponding sentiments as in the positive,        negative and neutral form, the dictionary may be used as a        source for looking them up in reviews, and optionally replacing        them with a matching actual meaning.        (e) A Suitability Phrase Identifier for identifying phrases        revealing suitability aspects in consumer reviews, wherein a        gazetteer containing predefined sets of words and patterns known        to be commonly used for explaining the same may be referenced        and utilized for identification, for example, the pattern:        ((<ADVERB>)?<ADJ>):opinion (<Noun>)?:category        (<Preposition>):prep (<Word>)+:suitableFor; that may match the        following phrases: “blazer is perfect for a business meeting”:        (perfect):opinion (for):prep (a business meeting):suitableFor;        and        “the toy was perfect for my 2 years old”: (perfect):opinion        (for):prep (my 2 years old): suitableFor.        (f) An Opinion Normalizer for aggregating opinions from consumer        reviews and normalizing the attitudes (i.e. appreciations or        critics) of the consumer, possibly in addition to normalizing        the underlying features (i.e. targets), wherein normalizing the        attitudes may include enriching them with root forms of the        words and contextually relevant synonyms and semantic        categories.        And/or, (g) a Sentiment Polarity Calculator for obtaining        sentiment polarity in consumer reviews, wherein based on the        product cluster to of a given product, and utilizing a        comparison API, other similar products in the cluster may be        fetched and their reviews aggregated, and the collective text        may provide context that is useful to determine the orientation        of opinion.

According to some embodiments of the present invention, knowledgeinferred from Consumer Review and Product Descriptions may be utilizedfor updating and/or providing feedback to various e-commerce systemsand/or users thereof, exemplary applications may include:

(a) Product Ontology Updating based on Consumer Review Opinions,wherein, regular expressions over annotated reviews may be utilized foridentifying unknown use cases of products, thus enabling the inferringof new knowledge about the corresponding reviewed products.For Example: the consumer language “its color is perfectly suitable fora business meeting” is cross correlated with the respective product'sspecifications (e.g. a Jacket), inferring that the following statementmay be added to the product's ontology (knowledge base): “light graycolor is preferred by professionals for business meetings”.(b) Inferring of Dynamic Facets from Consumer Reviews, wherein rulebased information engineering methods are utilized to process productdescriptions and consumer reviews, while searching for facets notexplicitly highlighted by the merchants of the product. For example: inthe consumer language “a perfect gift for infants”—the appraisalexpression (i.e. a perfect gift) is identified, and using the rulesbased information engineering, as described hereinbefore, convert theconcerning “noun(s)” into a facet (e.g. gift), and present the facet toconsumers/customers searching for products.(c) Identifying Products with Out-dated Features, wherein featuresrepresented as numerical values are tracked and normalized forcomparison purposes (e.g. Camera 7MP>5MP), newer models succeeding theprevious ones (e.g. iPhone 6 succeeding iPhone 5) are tracked, andphrases added to catalogs for such features are compared with the newerproducts of the same category and the knowledge base is modifiedaccordingly.(d) Updating Product Catalogs Using Consumer Reviews, wherein phraseswith positive sentiments in the reviews are identified and then‘injected’ back into the product catalog allowing searches to beperformed on newly discovered features.An exemplary overall system of processing consumer reviews to updateproduct catalogs may execute some or all of the following steps, and/orany combination thereof:

normalizing the text of consumer reviews to get rid of spell errors;

using a microblog friendly POS tagger to annotate tokens withgrammatical tags

using shallow parsing techniques to find out sources and appraisalexpressions that express attitudes and targets;

assigning appropriate weights to the reviews by identifying if a reviewwas written by end-users, professionals, manufacturers or spammers;

assigning to the respective features, a calculated polarity of attitudesincluding those represented by idioms;

recognizing phrases used for explaining suitability aspects, based onwhich new facets and suitability expressions are added to the productcatalogs; and

readjusting the ranking of products with out-dated features.

And/or (e) Real-time Analysis of Consumer Reviews to Avoid NegativeReviews, wherein text of reviews being written by consumers may bemonitored in real-time, consumer's dislikes (i.e. the target and theattitude) are identified in real-time (i.e. prior to the posting of thereview) within negative consumer reviews as they are being written oredited, the feature(s) of the product (e.g. color, size) to which thecomplaint is targeted are identified, and substantially similarproducts, not including the complained about features, or includingvariations of these features, are presented and offered to theconsumer/customer.

In FIG. 1A, there is shown, in accordance with some embodiments, a blockdiagram of an exemplary system for Automated Digital CatalogAugmentation. In the figure, the Automated Digital Catalog AugmentationSystem is shown to be functionally connected to a Digital CommercePlatform. The Digital Commerce Platform includes a digital/onlinecatalog application server(s) for receiving, optionally through theshown network gateway(s), customer and point of sale (POS) orders forofferings included in the digital catalog of the Digital CommercePlatform. The digital/online catalog application server(s) isfunctionally connected to a transaction and order fulfillment server(s)for executing the received customer and point of sale (POS) orders andfor accordingly updating the digital catalog. The digital/online catalogapplication server(s) is shown to be functionally connected to thefollowing data stores: a digital catalog code data store for storingcomputer executable code for the generation, application and/ormanagement of the digital catalog; a digital catalog offering detailsdata store for storing details and descriptions of product and/orservice offerings included in the digital catalog; and/or a digitalcatalog offerings related reviews/comments data store for storingfeedbacks from customers, consumers and/or other users ofproduct/service offerings in the digital catalog.

The digital/online catalog application server(s) shown, may furtherreceive, optionally through the shown network gateway(s),consumer/customer/other reviews/comments for offerings included in thedigital catalog of the Digital Commerce Platform. The digital/onlinecatalog application server(s) may store the received reviews/comments tothe digital catalog offering(s) related reviews/comments data store.

Customer and point of sale (POS) orders, and/or consumer/customer/otherreviews/comments, may be communicated to the servers over a closednetwork or a direct communication session (e.g. a POS network/direct/VPNconnection), and/or from computerized communication devices, over theInternet and through the network gateway(s) shown.

The computerized communication devices shown in the figure are providedas an example. Various computerized communicationdevices/systems/components may be utilized to relay/upload digitalcatalog offerings' orders and/or reviews/comments to the digital/onlinecatalog application server(s). Such devices/systems/components mayinclude, but are not limited to: computing platforms, personalcomputers, laptops, tablets, smartphones, smartwatches or other wearabledevices, Internet robots, smart house devices or appliances and/or anyother digital communication/networking able device/system. Furthermore,any of the above listed devices/systems/components may be utilized torelay/upload/post/share/endorse/like digital catalog offerings'reviews/comments/opinions to product/service review web-site(s),publications, blogs, social networks, immediate messaging platformchats.

The Automated Digital Catalog Augmentation System shown, includes areview/comment scrapper(s) for retrieving digital catalog offeringsreviews/comments from: the digital catalog offerings reviews/commentsdata store of the Digital Commerce Platform; and/or from one or moreproduct/service review web-site(s), publications, blogs, social networksand/or network messages/chats, accessed through the shown networkgateway(s) of the Automated Digital Catalog Augmentation System.

The retrieved digital catalog offerings reviews/comments are relayed toa review text normalizer for correcting grammar and spelling errors inthe text, normalizing the text and/or converting transliterated text tooriginal script/language based on knowledge in the domain of respectiveproduct/service offerings in the digital catalog.

Corrected and normalized reviews are processed by the shown ReviewCriteria and Sentiment Extractor, utilizing a natural languageprocessor(s) including a criteria extractor and a sentiment extractor,to identify within the corrected text of the offerings reviews/comments,criteria relating to offering(s) in the digital catalog and thesentiment expressed towards the new criteria within the review/comment.

The shown catalog augmentation and/or offering related sentimentreporting logic unit/module is utilized for estimating, for eachreceived review/comment, whether the review/comment is positive. And,for each review/comment estimated to be positive: (1) comparing thecriteria and sentiment, identified within the review/comment, toavailable digital catalog offering details received through the shownoffering details data reader of the catalog interface, wherein thereview/comment and the received available digital catalog offeringdetails relate to the product/service of the same offering; (2)generating, for identified review/comment criteria, not found (as partof the comparison) within the received available digital catalogoffering details, data records including the new criteria and sentimentas expressed within the review/comment; and (3) utilizing the offeringdetails data augmenter of the catalog interface to augment the digitalcatalog, by updating the digital catalog offering details data store ofthe Digital Commerce Platform with the generated data records includingthe new criteria and sentiment, thus triggering the addition of the newcriteria and sentiment to available/existing details/descriptions ofofferings in the catalog.

According to some embodiments, for received reviews/comments estimatedto be negative, the catalog augmentation and/or offering relatedsentiment reporting logic unit/module may: (1) augment the digitalcatalog with alternative products/services including variations tospecific features thereof, towards which the negative review sentimentwas expressed; and/or (2) remove from presentation, or deprioritize thepresentation (e.g. present as later/last catalog offerings option) of,offerings which were the target of the negative review/comment.

In FIG. 1B there is shown a block diagram of the exemplary system forAutomated Digital Catalog Augmentation of FIG. 1A, further comprising anoffering(s) related sentiment reporting-logic unit/module for compilingand relaying offering related sentiment reports, including details ofnegative sentiment expressed in a review/comment and thecriteria/feature of the product/offering towards which it was expressed,to offering's point(s) of contact (POC(s)) (e.g. retailer, producer,distributer).

In FIG. 1C there is shown, in accordance with some embodiments, aflowchart of the steps executed by the system of FIGS. 1A and 1B as partof an exemplary process for Automated Digital Catalog Augmentation. Theexemplary process shown includes the following steps: (1) Accessing adigital catalog data store and analyzing product or service offering(s)related data to compile a set of offerings; (2) Scanning through one ormore network/Internet accessible servers/data-stores/data-repositorieswhere offering(s) related reviews/comments are posted; (3) Correlatingspecific reviews/comments with corresponding offering(s) within the setcompiled from the catalog; (4) Using natural language processing and/orartificial intelligence to extract one or more offering(s) relatedcriteria discussed, assessed, evaluated and/or otherwise mentionedwithin one or more posted reviews/comments relating to the specificoffering(s); and/or (5) Using natural language processing and/orartificial intelligence to extract sentiment expressed in correspondenceto the extracted criteria for the specific offering(s).

Positive reviews/comments, including mostly or only positive sentimenttowards their respective extracted criteria, triggers: (6) theAugmenting of digital offering(s) related data for the specificoffering(s) within the digital catalog (data store), using extractedcriteria and/or corresponding positive sentiment.

Negative reviews/comments, including mostly or only negative sentimenttowards their respective extracted criteria, triggers (7) anycombination of the following: (a) Notifying/Reporting to a partyresponsible for the digital catalog, or offering(s) point of contact, ofextracted criteria with corresponding negative sentiment; (b) Stoppingor Deprioritizing the presentation, within the catalog, of catalogoffering(s) for which criteria with corresponding negative sentiment wasextracted; and/or (c) Presenting alternative catalog offering(s) withalternatives to specific criteria (e.g. product features relatedcriteria) for which corresponding negative sentiment was extracted.

In FIG. 2A there are shown in greater detail, in accordance with someembodiments of the present invention: a review/comment scrapper, areview text normalizer and a review criteria and sentiment extractor;the operation of which may be described in conjunction with the stepslisted in the flowcharts of FIGS. 2B-2D.

The review/comment scrapper of FIG. 2A includes: a web/network crawler,a data miner and/or a robot (bot), for finding and retrievingreviews/comments related to digital catalog offering(s) based onreceived details of digital catalog offering(s) for whichreviews/comments are to be scrapped. The details of digital catalogoffering(s) for which reviews/comments are to be scrapped may becommunicated, to the scrapper, by the ‘catalog augmentation’ and/or‘offering related sentiment reporting logic’ unit/module (not shown)based on data from the offering(s) details data reader of the cataloginterface (not shown).

Reviews/comments are shown to be scrapped from the ‘digital catalogoffering(s) related reviews/comments data store’ of the digital commerceplatform and/or from product/service review websites, publications,blogs, social networks and/or chat/IM messages.

The operation of the review text normalizer in FIG. 2A may be describedin conjunction with the steps listed in the flowchart of FIG. 2B. Theshown transliteration/mixed-code/multiple-language normalizer initiallyscans scrapped reviews for transliterated, mixed-code and/ormultiple-language occurrences, and normalizes the text by referencingknowledge base in various languages, codes, slangs and/or domains,converting the found text occurrences into an original script, in asingle/unified code-type/language.

The shown spell corrector may correct the spelling of the resultingnormalized text, optionally utilizing domain specific spellcheckingrules, matching the domain of the product/service for whichreviews/comments are analyzed.

The shown grammar corrector corrects grammatical inconsistencies inconsumer reviews/comments, optionally utilizing phrase level shallowparsing wherein words of interest in close proximity are shuffled (e.g.pseudo randomly) to try and convey meaning.

The operation of the review criteria and sentiment extractor in FIG. 2Amay be described in conjunction with the steps listed in the flowchartof FIGS. 2C and 2D. The shown review source (reviewer) extractoranalyzes, classifies and characterizes the sources (reviewers) of thecorrected and normalized reviews/comments, estimating if and to whatlevel the source is trustworthy/useful. The steps of the trustworthinessestimation process executed by the review source trustworthinessestimator shown in FIG. 2A, are listed and described in further detailin the flowchart of FIG. 2D. the listed steps include: (1) Measuring thesimilarity of a reviewer's review(s) to reviews made by other reviewers,wherein higher similarity level indicates a more trustworthy source; (2)Measuring the number of endorsements a reviewer's review(s) received,wherein a higher number of endorsements and/or more positive ones,indicate a more trustworthy source; (3) Measuring the level of focus ofa reviewer's reviews in specific domain(s), wherein a higher domainfocus level indicates a more trustworthy source; (4) Checking theweb/network place/origin of a reviewer's review(s) and attempting toestablish the reviewer's identity, wherein reviews from web/networkplaces other than the web/network place selling, or directly selling,the product/service of the digital catalog offering and/or reviews fromreviewer(s) whose identity was successfully established, indicate a moretrustworthy source; and/or (5) Measuring the length of the review,wherein shorter, or more concise, reviews indicate a more trustworthysource.

The review weight allocation logic shown in FIG. 2A, allocates weightsto reviews/comments and/or to criteria/sentiment extracted therefrom,based on an aggregated weight of corresponding reviewer'strustworthiness levels—as indicated by any combination of outcomes ofthe above described reviewer assessment steps.

Returning now to FIG. 2A, there are shown a criteria extractor and asentiment extractor, the operation of the criteria and sentimentextractors of FIG. 2A may be described in conjunction with the followingsteps listed in the flowchart of FIG. 2C. the listed steps include: (1)Identifying whether opinions/sentiments within reviews target theoverall product/service of the offering, or specific criteria/featuresthereof; (2) Identifying the specific parts, criteria, components and/orfeatures of the reviewed offering(s) targeted by the review; (3)Identifying idioms within reviews and referencing an idiom dictionary(e.g. digital/web dictionary/repository) to retrieve correspondingsentiment for identified idioms, optionally replacing identified idiomswith corresponding ‘regular’ language expressions; (4) Identifyingphrases revealing suitability aspects within reviews, by referencing adictionary/repository of predefined word sets patterns commonly used forexplaining suitability issues; (5) Aggregating opinions from multiplereviews and normalizing the sentiments/attitudes therein, optionallyenriching the sentiment/attitude expressions with root forms (e.g. rootform tags) of words and/or semantic categories thereof; and/or (6)utilizing a comparison API/logic for finding offerings/products/servicesbelonging to, or associated with, the same types/clusters ofofferings/products/services of those reviewed, retrieving and analyzingreviews of similar types, or similarly clustered,offerings/products/services, to provide further context/criteria andopinion attitude/sentiment for currently analyzed offering(s) reviews.

In FIG. 3A there is shown in greater detail, in accordance with someembodiments of the present invention, a ‘catalog augmentation and/or‘offering related sentiment reporting logic’ unit/module; the operationof which may be described in conjunction with the steps listed in theflowchart of FIG. 3B.

The ‘catalog augmentation and/or ‘offering related sentiment reportinglogic’ unit/module of FIG. 3A relays specific offering(s) related data,received from the catalog interface reader, to the system'sreview/comment scrapper described hereinbefore. In return, the ‘catalogofferings available criteria/details’ to ‘review(s) extracted criteria’comparison logic shown, receives criteria and sentiment of correspondingscrapped reviews, extracted by the review criteria and sentimentextractor described hereinbefore, optionally along with respectivereview allocated weights.

The ‘catalog offerings available criteria/details’ to ‘review(s)extracted criteria’ comparison logic compares catalog available/existingofferings related details data to extracted review criteria andsentiment(s), outputting new criteria/sentiments not yet in the catalogoffering details. The reviews weight factoring logic adjusts theweight(s) of the new criteria/sentiments based on the received reviewallocated weights. The digital catalog records generator, generates newdigital catalog records, based on the review(s)/comment(s) extractedcriteria/sentiments determined to be new (not yet in catalog offering)and the weights allocated thereto, and forwards them to the offering(s)details data augmenter of the catalog interface, for augmentation intoexisting digital catalog records.

The digital catalog records generator, may further generate, based onnegative offering review(s), new digital catalog records to cause thedigital catalog to stop, or deprioritize, the presentation of thecorresponding offering(s) in the digital catalog; and/or to presentadditional offering(s) with substitutes to the specificproduct/service/offering feature(s) towards which the negative sentimentin the review(s) was expressed.

The negative sentiment report generator, receives details ofproduct/service offering(s) and/or criteria/features thereof, thatreceived negative sentiment within the review(s); compiles acorresponding report listing at least the negative sentiment expressedwithin the review and the respective offering(s), or offering(s)criteria/features, which are the target of the negative sentiment; andrelays the report to one or more point(s) of contact associated with theproduct/service of the respective catalog offering(s). Review(s)extracted new criteria may include: use case, feature, usagecombination, use outcome and/or outdated feature, criteria types, asdescribed hereinbefore.

In FIGS. 4A-4D there are respectively shown: a digital catalog productdescription table, for an exemplary multipurpose pair of bicycle, priorto a review based augmentation/enrichment process (4A); an exemplarysystem scrapped review of the multipurpose pair of bicycle (4B);exemplary criteria and sentiments, extracted from the multipurpose pairof bicycle review (4C); and the digital catalog product descriptiontable, of the exemplary multipurpose pair of bicycle, following to areview based augmentation/enrichment process (4D).

The digital catalog product description table of FIG. 4A includes:digital catalog already-available offering descriptive text, the productcriteria it relates to and the sentiment of the criteria. In the FIG. 4Breview shown, extracted criteria and sentiments are highlighted over theentire text of the review. FIG. 4C lists for each of the reviewextracted criteria: the criteria type, the sentiment of the criteria,and the respective digital catalog augmentation, or negative sentimentreporting, action it triggers. In FIG. 4D the catalog productdescription table of FIG. 4A is shown following to the catalogaugmentation based on FIG. 4C criteria and sentiment.

The present invention may include a digital catalog augmentation systemwith a digital catalog interface module to read from a digital catalogdata storage, directly or indirectly, one or more catalog data recordsconstituting an offer listing within a digital catalog, wherein theoffer listing may include a description of a specific product or serviceoffering and/or links to execute a transaction relating to the offering.The system may also include a Review Criteria and Sentiment Extractor(RCSE) to identify and convert one or more reviews posted on a reviewforum into one or more data records used to augment the offer listingwithin the digital catalog. The catalog interface may further be adaptedto write to the digital catalog data storage, directly or indirectly,one or more catalog data records used to augment the offer listingwithin the digital catalog.

An offering listing within a digital catalog may be generated byrendering one or more data records and/or data files within a portion ofthe digital catalog. Offering listing is a description, optionally withpictures of the offering, a cost of the offering and/or instructions forpurchasing the offering. The digital catalog may part of and/orgenerated by a digital commerce platform, retail or online.

According to embodiments, augmenting the offer listing within a digitalcatalog, such as an online catalog, may include: (a) adding one or moredata records or files to be rendered as part of the offer listing in thedigital catalog; (b) editing one or more data records or files to berendered as part of the offer listing in the digital catalog; and (c)removing one or more data records or files to be rendered as part of theoffer listing in the digital catalog. An added or modifier record mayinclude at least one extracted offering related criteria and anextracted assessment or sentiment corresponding to the extractedcriteria. An added or modifier record may expand a feature matrixgenerated as part of the digital catalog offer listing.

An RCSE according to embodiments may include a scrapper to scan throughone or more review forums and to identify and copy text from one or morereviews relating to the offer listing in the digital catalog. Thescrapper may be adapted to scrape reviews posted to a review forumintegral or otherwise associated with a digital commerce platform of thedigital catalog. The scrapper may be adapted to scrape reviews posted toa review forum integral or otherwise associated with another digitalplatform such as an online blog, a reviews website, a social network, orany other server accessible through the internet.

An RCSE according to embodiment may include a natural language processorto extract from the copied text at least one offering related criteriaassociated with the offer listed in the digital catalog. The naturallanguage processor may further be adapted to extract from the copiedtext at least one assessment of or sentiment towards the listed offeringwithin a context of an extracted criteria.

As part of understanding and extracting information (offering relatedcriteria, assessment, sentiment, etc.) from a review of an offering, thenatural language processor and or another module integral or otherwisefunctionally associated with an RCSE, according to embodiments, mayapply normalization logic to correct transliterated (mixed-code)language in a review, by: (a) referencing a knowledge base in variouslanguages; or (b) converting transliterated review language intooriginal scripts utilizing domain specific spell correction to obtainthe nearest possible words. A natural language processor, or anothermodule integral or otherwise functionally associated with an RCSE,according to embodiments, may be context aware, such that said processorcross-correlates pre-stored feature or attribute information of anoffering which is a subject of a specific review, either a product or aservice, as part of processing the review for criteria and sentimentextraction about the offering.

A digital catalog augmentation system according to embodiments may, uponextraction of a threshold number of reviews with negative sentimenttowards a listed offering, may trigger one or more of: (a) generation ofa report; (b) a change in placement of the offer listing within thedigital catalog; (c) a change in catalog search engine result placement;and (d) a suspension or delisting of the offering from the catalog.According to further embodiments, detection of a review about anoffering being posted with negative sentiment towards an offering maytrigger an automated response to the review writer with a replacementoffer and or a monetary compensation offer.

A digital catalog augmentation system according to embodiments mayaugment an offer listing within a digital catalog by providing links toan alternate offering, wherein the alternate offering may be selected:(a) when one or more reviews indicate superior properties or attributesof the alternate offering; (b) when one or more reviews indicatedissatisfaction with one or more properties, features or attributes ofthe listed offering and the alternate has a higher rating correspondingto the one or more properties, features or attributes; (c) when one ormore reviews indicate an unmet expectation with regard to one or moreproperties, features, or attributes of the listed offering and thealternate offering is known to meet the expectation with regard to theone or more properties, features or attributes.

According to some embodiments, the digital catalog augmentation systemmay include a reviewer assessment module to assess a credibility of aposter of one or more offering reviews.

The subject matter described above is provided by way of illustrationonly and should not be constructed as limiting. While certain featuresof the invention have been illustrated and described herein, manymodifications, substitutions, changes, and equivalents will now occur tothose skilled in the art. It is, therefore, to be understood that theappended claims are intended to cover all such modifications and changesas fall within the true spirit of the invention.

1. A digital catalog augmentation system comprising: a digital cataloginterface module to read from a digital catalog data storage, directlyor indirectly, one or more catalog data records constituting an offerlisting within a digital catalog, wherein the offer listing includes adescription of a specific product or service offering; and a ReviewCriteria and Sentiment Extractor (RCSE) to identify and convert one ormore reviews posted on a review forum into one or more data records usedto augment the offer listing within the digital catalog.
 2. The digitalcatalog augmentation system according to claim 1, wherein said cataloginterface is further adapted to write to the digital catalog datastorage, directly or indirectly, one or more catalog data records usedto augment the offer listing within the digital catalog.
 3. The digitalcatalog augmentation system according to claim 1, wherein said RCSEcomprises a scrapper to scan through one or more review forums and toidentify and copy text from one or more reviews relating to the offerlisting in the digital catalog.
 4. The digital catalog augmentationsystem according to claim 1, wherein said scrapper is adapted to scrapereviews posted to a review forum integral or otherwise associated with adigital commerce platform of the digital catalog.
 5. The digital catalogaugmentation system according to claim 1, wherein said scrapper isadapted to scrape reviews posted to a review forum integral or otherwiseassociated with another digital platform such as an online blog, areviews website, a social network, or any other server accessiblethrough the internet.
 6. The digital catalog augmentation systemaccording to claim 3, wherein said RCSE includes a natural languageprocessor to extract from the copied text at least one offering relatedcriteria associated with the offer listed in the digital catalog.
 7. Thedigital catalog augmentation system according to claim 6, wherein saidnatural language processor is further adapted to extract from the copiedtext at least one assessment of or sentiment towards the listed offeringwithin a context of an extracted criteria.
 8. The digital catalogaugmentation system according to claim 6, wherein said natural languageprocessor is context aware, such that said processor cross-correlatespre-stored feature or attribute information of an offering which is asubject of a specific review, either a product or a service, as part ofprocessing the review for criteria and sentiment extraction about theoffering.
 9. The digital catalog augmentation system according to claim1, wherein augmenting the offer listing includes: (a) adding one or moredata records or files to be rendered as part of the offer listing in thedigital catalog; (b) editing one or more data records or files to berendered as part of the offer listing in the digital catalog; and (c)removing one or more data records or files to be rendered as part of theoffer listing in the digital catalog.
 10. The digital catalogaugmentation system according to claim 9, wherein an added or modifierrecord includes at least one extracted offering related criteria and anextracted assessment or sentiment corresponding to the extractedcriteria.
 11. The digital catalog augmentation system according to claim9, wherein an added or modifier record expands a feature matrixgenerated as part of the digital catalog offer listing.
 12. The digitalcatalog augmentation system according to claim 1, wherein extraction ofa threshold number of reviews with negative sentiment triggers one ormore of: (a) generation of a report; (b) a change in placement of theoffer listing within the digital catalog; (c) a change in catalog searchengine result placement; and (d) a suspension or delisting of theoffering from the catalog.
 13. The digital catalog augmentation systemaccording to claim 1, wherein said review extractor further comprises areviewer assessment module adapted to assess credibility of a poster ofone or more offering reviews.
 14. The digital catalog augmentationsystem according to claim 1, wherein the review extractor furthercomprises review normalization logic to correct transliterated(mixed-code) language by: (a) referencing a knowledge base in variouslanguages; or (b) converting transliterated review language intooriginal scripts utilizing domain specific spell correction to obtainthe nearest possible words.
 15. The digital catalog augmentation systemaccording to claim 1, wherein detection of a review about an offeringbeing posted with negative sentiment towards the offering triggers anautomated response to the review writer with a replacement offer and ora monetary compensation offer.
 16. The digital catalog augmentationsystem according to claim 1, wherein augmenting the offer listing withinthe digital catalog includes providing links to an alternate offering,wherein the alternate offering is selected: (a) when one or more reviewsindicate superior properties or attributes of the alternate offering;(b) when one or more reviews indicate dissatisfaction with one or moreproperties, features or attributes of the listed offering and thealternate has a higher rating corresponding to the one or moreproperties, features or attributes; (c) when one or more reviewsindicate an unmet expectation with regard to one or more properties,features, or attributes of the listed offering and the alternateoffering is known to meet the expectation with regard to the one or moreproperties, features or attributes.